from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.solution import read_solutions
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT1()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
max_evaluations = 25000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.run()
front = algorithm.get_result()
Warning
This requires some extra dependencies
from dask.distributed import Client
from distributed import LocalCluster
from examples.multiobjective.parallel.zdt1_modified import ZDT1Modified
from jmetal.algorithm.multiobjective.nsgaii import DistributedNSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT1Modified()
# setup Dask client
client = Client(LocalCluster(n_workers=24))
ncores = sum(client.ncores().values())
print(f'{ncores} cores available')
# creates the algorithm
max_evaluations = 25000
algorithm = DistributedNSGAII(
problem=problem,
population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
number_of_cores=ncores,
client=client
)
algorithm.run()
front = algorithm.get_result()
from jmetal.algorithm.multiobjective.nsgaii import DynamicNSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.problem.multiobjective.fda import FDA2
from jmetal.util.observable import TimeCounter
from jmetal.util.observer import PlotFrontToFileObserver, WriteFrontToFileObserver
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = FDA2()
time_counter = TimeCounter(delay=1)
time_counter.observable.register(problem)
time_counter.start()
max_evaluations = 25000
algorithm = DynamicNSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.observable.register(observer=PlotFrontToFileObserver('front_vis'))
algorithm.observable.register(observer=WriteFrontToFileObserver('front_files'))
algorithm.run()
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT2
from jmetal.util.solutions import read_solutions
from jmetal.util.solutions.comparator import GDominanceComparator
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT2()
problem.reference_front = read_solutions(filename='resources/reference_front/ZDT2.pf')
reference_point = [0.2, 0.5]
max_evaluations = 25000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
dominance_comparator=GDominanceComparator(reference_point),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.run()
front = algorithm.get_result()
jmetal.algorithm.multiobjective.nsgaii.
DistributedNSGAII
(problem: jmetal.core.problem.Problem, population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, number_of_cores: int, client: distributed.client.Client, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]¶Bases: jmetal.core.algorithm.Algorithm
create_initial_solutions
() → List[S][source]¶Creates the initial list of solutions of a metaheuristic.
jmetal.algorithm.multiobjective.nsgaii.
DynamicNSGAII
(problem: jmetal.core.problem.DynamicProblem[~S][S], population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.DominanceComparator = <jmetal.util.comparator.DominanceComparator object>)[source]¶Bases: jmetal.algorithm.multiobjective.nsgaii.NSGAII
, jmetal.core.algorithm.DynamicAlgorithm
jmetal.algorithm.multiobjective.nsgaii.
NSGAII
(problem: jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: jmetal.core.operator.Mutation, crossover: jmetal.core.operator.Crossover, selection: jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)[source]¶Bases: jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm
replacement
(population: List[S], offspring_population: List[S]) → List[List[S]][source]¶This method joins the current and offspring populations to produce the population of the next generation by applying the ranking and crowding distance selection.
population – Parent population.
offspring_population – Offspring population.
New population after ranking and crowding distance selection is applied.
jmetal.algorithm.multiobjective.nsgaii.
R
= ~R¶